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added jquery and a few more test notebooks
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Abubakar Abid
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9ecba1b89f
commit
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746
Test Pytorch.ipynb
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746
Test Pytorch.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import torchvision\n",
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"import torchvision.transforms as transforms\n",
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"import gradio"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Device configuration\n",
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"device = torch.device('cpu')\n",
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"\n",
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"# Hyper-parameters \n",
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"input_size = 784\n",
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"hidden_size = 500\n",
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"num_classes = 10\n",
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"num_epochs = 2\n",
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"batch_size = 100\n",
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"learning_rate = 0.001\n",
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"\n",
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"# MNIST dataset \n",
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"train_dataset = torchvision.datasets.MNIST(root='../../data', train=True, transform=transforms.ToTensor(), download=True)\n",
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"test_dataset = torchvision.datasets.MNIST(root='../../data',train=False, transform=transforms.ToTensor())\n",
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"train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size,shuffle=True)\n",
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"test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch [1/2], Step [100/600], Loss: 0.4317\n",
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"Epoch [1/2], Step [200/600], Loss: 0.2267\n",
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"Epoch [1/2], Step [300/600], Loss: 0.2052\n",
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"Epoch [1/2], Step [400/600], Loss: 0.1179\n",
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"Epoch [1/2], Step [500/600], Loss: 0.1108\n",
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"Epoch [1/2], Step [600/600], Loss: 0.1830\n",
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"Epoch [2/2], Step [100/600], Loss: 0.0972\n",
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"Epoch [2/2], Step [200/600], Loss: 0.0662\n",
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"Epoch [2/2], Step [300/600], Loss: 0.1487\n",
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"Epoch [2/2], Step [400/600], Loss: 0.0640\n",
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"Epoch [2/2], Step [500/600], Loss: 0.0425\n",
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"Epoch [2/2], Step [600/600], Loss: 0.0979\n"
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]
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}
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],
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"source": [
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"# Fully connected neural network with one hidden layer\n",
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"class NeuralNet(nn.Module):\n",
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" def __init__(self, input_size, hidden_size, num_classes):\n",
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" super(NeuralNet, self).__init__()\n",
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" self.fc1 = nn.Linear(input_size, hidden_size) \n",
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" self.relu = nn.ReLU()\n",
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" self.fc2 = nn.Linear(hidden_size, num_classes) \n",
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" \n",
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" def forward(self, x):\n",
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" out = self.fc1(x)\n",
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" out = self.relu(out)\n",
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" out = self.fc2(out)\n",
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" return out\n",
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"\n",
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"model = NeuralNet(input_size, hidden_size, num_classes).to(device)\n",
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"\n",
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"# Loss and optimizer\n",
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"criterion = nn.CrossEntropyLoss()\n",
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"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) \n",
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"\n",
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"# Train the model\n",
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"total_step = len(train_loader)\n",
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"for epoch in range(num_epochs):\n",
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" for i, (images, labels) in enumerate(train_loader): \n",
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" # Move tensors to the configured device\n",
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" images = images.reshape(-1, 28*28).to(device)\n",
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" labels = labels.to(device)\n",
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" \n",
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" # Forward pass\n",
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" outputs = model(images)\n",
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" loss = criterion(outputs, labels)\n",
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" \n",
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" # Backward and optimize\n",
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" optimizer.zero_grad()\n",
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" loss.backward()\n",
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" optimizer.step()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Accuracy of the network on the 10000 test images: 97.04 %\n"
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]
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}
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],
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"source": [
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"# Test the model\n",
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"# In test phase, we don't need to compute gradients (for memory efficiency)\n",
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"with torch.no_grad():\n",
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" correct = 0\n",
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" total = 0\n",
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" for images, labels in test_loader:\n",
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" images = images.reshape(-1, 28*28).to(device)\n",
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" labels = labels.to(device)\n",
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" outputs = model(images)\n",
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" _, predicted = torch.max(outputs.data, 1)\n",
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" total += labels.size(0)\n",
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" correct += (predicted == labels).sum().item()\n",
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"\n",
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" print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"torch.float64\n",
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"torch.float64\n"
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]
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},
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{
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"ename": "RuntimeError",
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"evalue": "Expected object of type torch.FloatTensor but found type torch.DoubleTensor for argument #4 'mat1'",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m<ipython-input-39-d6583191b5ef>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mVariable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mprediction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 475\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 476\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 477\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 478\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 479\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m<ipython-input-9-abba6ac73cbf>\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfc1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfc2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 475\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 476\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 477\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 478\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 479\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\linear.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 54\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 56\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 57\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\functional.py\u001b[0m in \u001b[0;36mlinear\u001b[1;34m(input, weight, bias)\u001b[0m\n\u001b[0;32m 1022\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mbias\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1023\u001b[0m \u001b[1;31m# fused op is marginally faster\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1024\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maddmm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1025\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1026\u001b[0m \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mRuntimeError\u001b[0m: Expected object of type torch.FloatTensor but found type torch.DoubleTensor for argument #4 'mat1'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"value = torch.from_numpy(images.numpy())\n",
|
||||
"print(value.dtype)\n",
|
||||
"value = torch.autograd.Variable(value)\n",
|
||||
"print(value.dtype)\n",
|
||||
"prediction = model(value)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"dtype('float64')"
|
||||
]
|
||||
},
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"images.numpy().astype('float64').dtype"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(100, 10)"
|
||||
]
|
||||
},
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"prediction.data.numpy().shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[-2.94313431e+00, -1.81460023e+00, -2.08448991e-01,\n",
|
||||
" -2.29123878e+00, -2.91417217e+00, -7.30904102e-01,\n",
|
||||
" -1.85286796e+00, -8.89607048e+00, 3.85826755e+00,\n",
|
||||
" -5.70444298e+00],\n",
|
||||
" [-5.27852488e+00, -9.87475681e+00, -3.23101878e+00,\n",
|
||||
" -3.27192068e+00, 2.99915814e+00, -4.19678402e+00,\n",
|
||||
" -6.34950256e+00, -4.51865005e+00, 7.18662143e-01,\n",
|
||||
" 4.91613436e+00],\n",
|
||||
" [ 5.31619835e+00, -4.94643354e+00, -7.60741353e-01,\n",
|
||||
" -3.37821364e+00, -2.58448744e+00, -1.16258490e+00,\n",
|
||||
" -2.44758511e+00, -2.42502451e+00, -2.97429585e+00,\n",
|
||||
" -8.71329665e-01],\n",
|
||||
" [-6.26879740e+00, 5.35215139e+00, -1.39423239e+00,\n",
|
||||
" -3.57356954e+00, -1.04397392e+00, -6.51621342e+00,\n",
|
||||
" -5.03530502e+00, -3.36044490e-01, -1.06999171e+00,\n",
|
||||
" -5.35540390e+00],\n",
|
||||
" [ 5.72689712e-01, -4.73341894e+00, 9.67390776e-01,\n",
|
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" -7.11784005e-01, -2.87459540e+00, -3.85147333e-03,\n",
|
||||
" -1.63910186e+00, -3.20800948e+00, -1.86211896e+00,\n",
|
||||
" -5.54116011e+00],\n",
|
||||
" [-2.28098822e+00, -5.37271118e+00, 1.50332046e+00,\n",
|
||||
" 1.23391628e+00, -8.18955231e+00, -7.10122824e+00,\n",
|
||||
" -9.54822731e+00, 2.04598665e+00, 2.21477568e-01,\n",
|
||||
" -5.82763791e-01],\n",
|
||||
" [-9.81631875e-02, -4.68611860e+00, 4.79472011e-01,\n",
|
||||
" -5.89810753e+00, 4.02780437e+00, -2.99009085e+00,\n",
|
||||
" 9.27805245e-01, -3.35206652e+00, -2.87583947e+00,\n",
|
||||
" -3.54016685e+00],\n",
|
||||
" [ 7.91082382e-02, -3.29304123e+00, -3.03544235e+00,\n",
|
||||
" -4.35647297e+00, -2.58279252e+00, 5.38625240e+00,\n",
|
||||
" -6.60099745e-01, -4.54817867e+00, 3.72485667e-01,\n",
|
||||
" -5.45329714e+00],\n",
|
||||
" [-3.40048730e-01, -2.23622179e+00, -1.75288630e+00,\n",
|
||||
" -4.22681570e+00, -5.96652508e-01, 9.88374472e-01,\n",
|
||||
" 9.12128639e+00, -6.91706181e+00, -5.71193886e+00,\n",
|
||||
" -7.31577396e+00],\n",
|
||||
" [-3.58501768e+00, -9.25465584e+00, -5.46614408e-01,\n",
|
||||
" -2.43667293e+00, -6.48066759e+00, -3.89760876e+00,\n",
|
||||
" -1.38017788e+01, 9.51254082e+00, -2.95755482e+00,\n",
|
||||
" 2.09405303e+00],\n",
|
||||
" [-1.75172710e+00, -2.98126078e+00, -6.65290546e+00,\n",
|
||||
" -2.85864210e+00, -5.55760241e+00, 2.44312382e+00,\n",
|
||||
" -3.61811829e+00, -4.92458248e+00, 4.85441971e+00,\n",
|
||||
" -3.99161577e+00],\n",
|
||||
" [ 7.10333920e+00, -5.87376070e+00, 7.35742152e-01,\n",
|
||||
" -4.57388163e+00, -5.62757587e+00, -8.69627833e-01,\n",
|
||||
" -3.81240129e+00, -1.22680414e+00, -5.86168003e+00,\n",
|
||||
" -3.06198215e+00],\n",
|
||||
" [-4.35662460e+00, 7.14129639e+00, 4.32708621e-01,\n",
|
||||
" -1.88450491e+00, -3.92650890e+00, -4.76905346e+00,\n",
|
||||
" -4.78737926e+00, -2.06425619e+00, -1.43031192e+00,\n",
|
||||
" -8.51775265e+00],\n",
|
||||
" [-5.01732492e+00, -4.75002670e+00, 7.48586702e+00,\n",
|
||||
" 8.71298671e-01, -6.77810001e+00, -4.56456995e+00,\n",
|
||||
" -5.64565229e+00, -2.32957077e+00, 1.09462869e+00,\n",
|
||||
" -5.92619801e+00],\n",
|
||||
" [-3.25442362e+00, -3.75993347e+00, -3.17320156e+00,\n",
|
||||
" 4.08569860e+00, -8.89118862e+00, -1.56907606e+00,\n",
|
||||
" -1.29745827e+01, -4.13903046e+00, 1.30396795e+00,\n",
|
||||
" 4.25274998e-01],\n",
|
||||
" [-3.55768895e+00, -2.09418583e+00, -1.02781892e+00,\n",
|
||||
" -6.95499659e+00, 6.68295813e+00, -2.23202968e+00,\n",
|
||||
" -2.39104450e-01, -4.58472347e+00, -4.44918251e+00,\n",
|
||||
" -5.70568848e+00],\n",
|
||||
" [-3.22387075e+00, -1.00904818e+01, -1.66163158e+00,\n",
|
||||
" -4.08348942e+00, -3.42650390e+00, -3.48878241e+00,\n",
|
||||
" -1.04407053e+01, 6.01407433e+00, -1.70194793e+00,\n",
|
||||
" 3.92319489e+00],\n",
|
||||
" [-2.36084223e+00, -7.12867594e+00, 2.79588461e-01,\n",
|
||||
" -3.45690346e+00, -3.48034048e+00, -2.39581585e+00,\n",
|
||||
" -2.31899548e+00, -7.42060089e+00, 8.48381615e+00,\n",
|
||||
" -6.04322863e+00],\n",
|
||||
" [-4.19490576e+00, -1.02733526e+01, -1.44479012e+00,\n",
|
||||
" -4.82172012e+00, 3.25319171e-02, -3.11602783e+00,\n",
|
||||
" -6.72438049e+00, 3.06269407e-01, -1.48180246e+00,\n",
|
||||
" 4.33638811e+00],\n",
|
||||
" [-2.75081682e+00, -7.28020811e+00, -2.98303461e+00,\n",
|
||||
" -2.76366043e+00, -4.09473085e+00, -3.54056692e+00,\n",
|
||||
" -1.37984486e+01, 8.48108864e+00, -4.28329992e+00,\n",
|
||||
" 3.44067287e+00],\n",
|
||||
" [-1.47935200e+00, -4.31553364e+00, -1.80156577e+00,\n",
|
||||
" -3.10084033e+00, -7.65861988e+00, -2.25040245e+00,\n",
|
||||
" -5.25622416e+00, -6.60806179e+00, 6.59777069e+00,\n",
|
||||
" -3.74126458e+00],\n",
|
||||
" [-3.09584522e+00, -4.03994560e+00, 1.39546502e+00,\n",
|
||||
" -1.71985483e+00, -3.30831736e-01, -9.78809655e-01,\n",
|
||||
" 5.82206869e+00, -6.38060808e+00, -4.40905428e+00,\n",
|
||||
" -5.61296463e+00],\n",
|
||||
" [-4.33585930e+00, -2.28015685e+00, -6.50762844e+00,\n",
|
||||
" -7.50386524e+00, 3.49900460e+00, -1.83042765e-01,\n",
|
||||
" -4.88598967e+00, -2.54932785e+00, -1.70414543e+00,\n",
|
||||
" -1.71335429e-01],\n",
|
||||
" [-6.33788157e+00, 8.20950222e+00, -1.09110951e+00,\n",
|
||||
" -4.89209270e+00, -2.66071391e+00, -5.96939754e+00,\n",
|
||||
" -4.53781509e+00, -1.31869841e+00, -2.04262447e+00,\n",
|
||||
" -7.43765354e+00],\n",
|
||||
" [-1.98968935e+00, -1.08618965e+01, -1.73519349e+00,\n",
|
||||
" -3.69034433e+00, 7.34611869e-01, -3.01101327e+00,\n",
|
||||
" -8.48536491e+00, 1.87765157e+00, -1.50498271e+00,\n",
|
||||
" 4.90581846e+00],\n",
|
||||
" [-3.59241199e+00, -2.45339823e+00, 2.14817572e+00,\n",
|
||||
" 9.27214742e-01, -8.07486057e+00, -2.06884146e+00,\n",
|
||||
" -7.01898956e+00, -1.67429686e+00, 1.15502942e+00,\n",
|
||||
" -4.88388157e+00],\n",
|
||||
" [-5.33111954e+00, -1.68560290e+00, 4.15555894e-01,\n",
|
||||
" -1.83269072e+00, -2.49122381e+00, -1.24994302e+00,\n",
|
||||
" -1.90831006e+00, -3.09089851e+00, 3.47727752e+00,\n",
|
||||
" -5.99055147e+00],\n",
|
||||
" [-2.52312398e+00, -6.40426540e+00, 2.39003658e+00,\n",
|
||||
" -5.73811722e+00, 4.70678949e+00, -5.00276566e+00,\n",
|
||||
" 2.86472030e-04, -3.41072738e-01, -4.31854200e+00,\n",
|
||||
" -1.90522814e+00],\n",
|
||||
" [-3.09813952e+00, -7.31600761e+00, 1.46198285e+00,\n",
|
||||
" -6.91232681e+00, 6.62286377e+00, -3.52320147e+00,\n",
|
||||
" -2.95166254e+00, -1.69830823e+00, -3.61260891e+00,\n",
|
||||
" -3.76430154e-03],\n",
|
||||
" [-3.90675402e+00, -8.58440208e+00, 3.84091437e-01,\n",
|
||||
" -9.11678314e-01, -8.35619164e+00, -5.30601501e+00,\n",
|
||||
" -1.34841938e+01, 7.37753201e+00, -1.02802634e+00,\n",
|
||||
" 3.48227167e+00],\n",
|
||||
" [ 8.09841061e+00, -5.84553242e+00, -4.36034203e-02,\n",
|
||||
" -3.31476593e+00, -7.94556332e+00, -1.81487560e+00,\n",
|
||||
" -1.08142841e+00, -4.74964380e+00, -3.62896776e+00,\n",
|
||||
" -3.67098570e+00],\n",
|
||||
" [-4.82872438e+00, 6.24776268e+00, -2.81209302e+00,\n",
|
||||
" -3.99583030e+00, -4.35030222e+00, -5.47072029e+00,\n",
|
||||
" -5.74521732e+00, -6.83430016e-01, -2.22886491e+00,\n",
|
||||
" -4.28679466e+00],\n",
|
||||
" [-1.13239086e+00, -1.07505608e+01, -3.85221720e-01,\n",
|
||||
" -4.16249514e+00, 1.11317813e-01, -5.01096535e+00,\n",
|
||||
" -7.09929132e+00, 5.47274947e-01, -2.61468601e+00,\n",
|
||||
" 5.91940689e+00],\n",
|
||||
" [-4.76688623e+00, -3.39046216e+00, 8.61355019e+00,\n",
|
||||
" -9.85053182e-02, -2.67433786e+00, -3.72860909e+00,\n",
|
||||
" -2.70728278e+00, -5.08575344e+00, -2.89577341e+00,\n",
|
||||
" -6.25328112e+00],\n",
|
||||
" [-3.26012516e+00, -3.56679535e+00, -2.13104582e+00,\n",
|
||||
" -4.59061265e-01, -5.79459000e+00, -1.60959554e+00,\n",
|
||||
" -5.09219933e+00, -7.62273407e+00, 6.20947170e+00,\n",
|
||||
" -3.95186377e+00],\n",
|
||||
" [-1.31348062e+00, -5.19767284e+00, -1.56831324e-01,\n",
|
||||
" -1.34070158e+00, -8.09649467e+00, -4.45510674e+00,\n",
|
||||
" -1.38942327e+01, 8.03822708e+00, -4.33768272e+00,\n",
|
||||
" 2.58261514e+00],\n",
|
||||
" [-4.15833044e+00, -5.74338055e+00, -5.63697433e+00,\n",
|
||||
" -1.24962544e+00, -8.88556576e+00, 2.62740111e+00,\n",
|
||||
" -8.16130829e+00, -6.14461994e+00, 5.57290173e+00,\n",
|
||||
" -2.04997277e+00],\n",
|
||||
" [-3.57854271e+00, -4.63059044e+00, 7.85657692e+00,\n",
|
||||
" 1.90798604e+00, -3.72001743e+00, -2.77965403e+00,\n",
|
||||
" -2.73498774e+00, -5.69463062e+00, -3.98288202e+00,\n",
|
||||
" -8.08887482e+00],\n",
|
||||
" [ 5.72618544e-01, -2.57825613e+00, -1.72792041e+00,\n",
|
||||
" -3.51139021e+00, -1.85640740e+00, 1.42014265e+00,\n",
|
||||
" 8.85237503e+00, -6.99086475e+00, -6.19104099e+00,\n",
|
||||
" -8.19126129e+00],\n",
|
||||
" [ 8.68018246e+00, -7.40369701e+00, -2.29292154e+00,\n",
|
||||
" -4.26178265e+00, -4.36462879e+00, -4.42296028e-01,\n",
|
||||
" -1.77303386e+00, -1.92960644e+00, -5.18078184e+00,\n",
|
||||
" -3.03363776e+00],\n",
|
||||
" [ 8.04516435e-01, -2.99887037e+00, -7.78589845e-01,\n",
|
||||
" -6.35569668e+00, 2.63802457e+00, -2.18808126e+00,\n",
|
||||
" 3.06124830e+00, -7.12826371e-01, -6.37444162e+00,\n",
|
||||
" -3.09541106e+00],\n",
|
||||
" [-2.78797674e+00, -6.94107354e-01, -3.76091480e+00,\n",
|
||||
" -1.08733892e-01, -4.78449726e+00, 2.34188890e+00,\n",
|
||||
" 1.54788947e+00, -5.22505283e+00, -2.23338032e+00,\n",
|
||||
" -4.30411434e+00],\n",
|
||||
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" [-6.58041859e+00, -1.57953396e-01, -4.17795897e-01,\n",
|
||||
" 8.16717339e+00, -1.60149975e+01, 1.75709569e+00,\n",
|
||||
" -1.52297287e+01, -5.96669102e+00, -4.64139891e+00,\n",
|
||||
" -9.46015775e-01],\n",
|
||||
" [-7.29091740e+00, -9.98701859e+00, -4.99693775e+00,\n",
|
||||
" -6.66315222e+00, 8.48139668e+00, -5.61966324e+00,\n",
|
||||
" -4.35089779e+00, -1.19920588e+00, 5.65500855e-01,\n",
|
||||
" 1.40877223e+00],\n",
|
||||
" [-3.93445063e+00, -3.84826946e+00, -6.23737240e+00,\n",
|
||||
" -6.25728607e+00, -3.60181952e+00, 1.01498127e+01,\n",
|
||||
" -2.82543993e+00, -7.06774330e+00, 1.51875269e+00,\n",
|
||||
" -8.45338631e+00],\n",
|
||||
" [-4.64281917e-01, -6.07886744e+00, -5.69949031e-01,\n",
|
||||
" -4.25031662e+00, 5.37574291e-01, -5.09637237e-01,\n",
|
||||
" 1.14183540e+01, -9.18782711e+00, -6.76804829e+00,\n",
|
||||
" -9.76811504e+00]], dtype=float32)"
|
||||
]
|
||||
},
|
||||
"execution_count": 42,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"float32\n",
|
||||
"torch.float32\n",
|
||||
"float32\n",
|
||||
"torch.float32\n",
|
||||
"float32\n",
|
||||
"torch.float32\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"prediction.data.numpy()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"inp = gradio.inputs.Sketchpad(flatten=True, scale=1/255, dtype='float32')\n",
|
||||
"io = gradio.Interface(inputs=inp, outputs=\"label\", model_type=\"pytorch\", model=model)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"No validation samples for this interface... skipping validation.\n",
|
||||
"NOTE: Gradio is in beta stage, please report all bugs to: a12d@stanford.edu\n",
|
||||
"Model is running locally at: http://localhost:7874/interface.html\n",
|
||||
"To create a public link, set `share=True` in the argument to `launch()`\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <iframe\n",
|
||||
" width=\"1000\"\n",
|
||||
" height=\"500\"\n",
|
||||
" src=\"http://localhost:7874/interface.html\"\n",
|
||||
" frameborder=\"0\"\n",
|
||||
" allowfullscreen\n",
|
||||
" ></iframe>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.IFrame at 0x14509666898>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(<gradio.networking.serve_files_in_background.<locals>.HTTPServer at 0x1450966be48>,\n",
|
||||
" 'http://localhost:7874/',\n",
|
||||
" None)"
|
||||
]
|
||||
},
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"float32\n",
|
||||
"torch.float32\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"io.launch()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
211
Test Sklearn.ipynb
Normal file
211
Test Sklearn.ipynb
Normal file
@ -0,0 +1,211 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"The autoreload extension is already loaded. To reload it, use:\n",
|
||||
" %reload_ext autoreload\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2\n",
|
||||
"\n",
|
||||
"from sklearn import datasets, svm\n",
|
||||
"import gradio\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"# The digits dataset\n",
|
||||
"digits = datasets.load_digits()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
|
||||
" decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rbf',\n",
|
||||
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
|
||||
" tol=0.001, verbose=False)"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# To apply a classifier on this data, we need to flatten the image, to\n",
|
||||
"# turn the data in a (samples, feature) matrix:\n",
|
||||
"n_samples = len(digits.images)\n",
|
||||
"data = digits.images.reshape((n_samples, -1))\n",
|
||||
"\n",
|
||||
"# Create a classifier: a support vector classifier\n",
|
||||
"classifier = svm.SVC(gamma=0.001)\n",
|
||||
"\n",
|
||||
"# We learn the digits on the first half of the digits\n",
|
||||
"classifier.fit(data[:n_samples // 2], digits.target[:n_samples // 2])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"16.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data.max()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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\n",
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 4 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"images_and_labels = list(zip(digits.images, digits.target))\n",
|
||||
"for index, (image, label) in enumerate(images_and_labels[:4]):\n",
|
||||
" plt.subplot(2, 4, index + 1)\n",
|
||||
" plt.axis('off')\n",
|
||||
" plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')\n",
|
||||
" plt.title('Training: %i' % label)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"classifier.predict()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"expected = digits.target[n_samples // 2:]\n",
|
||||
"predicted = classifier.predict(data[n_samples // 2:])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"inp = gradio.inputs.Sketchpad(shape=(8, 8), flatten=True, scale=16/255, invert_colors=False)\n",
|
||||
"io = gradio.Interface(inputs=inp, outputs=\"label\", model_type=\"sklearn\", model=classifier)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"No validation samples for this interface... skipping validation.\n",
|
||||
"NOTE: Gradio is in beta stage, please report all bugs to: a12d@stanford.edu\n",
|
||||
"Model is running locally at: http://localhost:7865/interface.html\n",
|
||||
"To create a public link, set `share=True` in the argument to `launch()`\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <iframe\n",
|
||||
" width=\"1000\"\n",
|
||||
" height=\"500\"\n",
|
||||
" src=\"http://localhost:7865/interface.html\"\n",
|
||||
" frameborder=\"0\"\n",
|
||||
" allowfullscreen\n",
|
||||
" ></iframe>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.IFrame at 0x2a051defdd8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(<gradio.networking.serve_files_in_background.<locals>.HTTPServer at 0x2a051e271d0>,\n",
|
||||
" 'http://localhost:7865/',\n",
|
||||
" None)"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"io.launch()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6 (tensorflow)",
|
||||
"language": "python",
|
||||
"name": "tensorflow"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
196
Test Tensorflow.ipynb
Normal file
196
Test Tensorflow.ipynb
Normal file
@ -0,0 +1,196 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2\n",
|
||||
"\n",
|
||||
"import tensorflow as tf\n",
|
||||
"import gradio"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"n_classes = 10\n",
|
||||
"(x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
|
||||
"x_train, x_test = x_train.reshape(-1, 784) / 255.0, x_test.reshape(-1, 784) / 255.0\n",
|
||||
"y_train = tf.keras.utils.to_categorical(y_train, n_classes).astype(float)\n",
|
||||
"y_test = tf.keras.utils.to_categorical(y_test, n_classes).astype(float)\n",
|
||||
"\n",
|
||||
"learning_rate = 0.5\n",
|
||||
"epochs = 5\n",
|
||||
"batch_size = 100"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"x = tf.placeholder(tf.float32, [None, 784], name=\"x\")\n",
|
||||
"y = tf.placeholder(tf.float32, [None, 10], name=\"y\")\n",
|
||||
"\n",
|
||||
"W1 = tf.Variable(tf.random_normal([784, 300], stddev=0.03), name='W1')\n",
|
||||
"b1 = tf.Variable(tf.random_normal([300]), name='b1')\n",
|
||||
"W2 = tf.Variable(tf.random_normal([300, 10], stddev=0.03), name='W2')\n",
|
||||
"hidden_out = tf.add(tf.matmul(x, W1), b1)\n",
|
||||
"hidden_out = tf.nn.relu(hidden_out)\n",
|
||||
"y_ = tf.matmul(hidden_out, W2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"probs = tf.nn.softmax(y_)\n",
|
||||
"cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=y_, labels=y))\n",
|
||||
"optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cross_entropy)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"init_op = tf.global_variables_initializer()\n",
|
||||
"correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
|
||||
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch: 1 cost = 0.317\n",
|
||||
"Epoch: 2 cost = 0.123\n",
|
||||
"Epoch: 3 cost = 0.086\n",
|
||||
"Epoch: 4 cost = 0.066\n",
|
||||
"Epoch: 5 cost = 0.052\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sess = tf.Session()\n",
|
||||
"sess.run(init_op)\n",
|
||||
"total_batch = int(len(y_train) / batch_size)\n",
|
||||
"for epoch in range(epochs):\n",
|
||||
" avg_cost = 0\n",
|
||||
" for start, end in zip(range(0, len(y_train), batch_size), range(batch_size, len(y_train)+1, batch_size)): \n",
|
||||
" batch_x = x_train[start: end]\n",
|
||||
" batch_y = y_train[start: end]\n",
|
||||
" _, c = sess.run([optimizer, cross_entropy], feed_dict={x: batch_x, y: batch_y})\n",
|
||||
" avg_cost += c / total_batch"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def predict(inp):\n",
|
||||
" return sess.run(probs, feed_dict={x:inp})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"inp = gradio.inputs.Sketchpad(flatten=True)\n",
|
||||
"io = gradio.Interface(inputs=inp, outputs=\"label\", model_type=\"pyfunc\", model=predict)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"No validation samples for this interface... skipping validation.\n",
|
||||
"NOTE: Gradio is in beta stage, please report all bugs to: a12d@stanford.edu\n",
|
||||
"Model is running locally at: http://localhost:7868/interface.html\n",
|
||||
"To create a public link, set `share=True` in the argument to `launch()`\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <iframe\n",
|
||||
" width=\"1000\"\n",
|
||||
" height=\"500\"\n",
|
||||
" src=\"http://localhost:7868/interface.html\"\n",
|
||||
" frameborder=\"0\"\n",
|
||||
" allowfullscreen\n",
|
||||
" ></iframe>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.lib.display.IFrame at 0x2a126711048>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(<gradio.networking.serve_files_in_background.<locals>.HTTPServer at 0x2a1266b6b38>,\n",
|
||||
" 'http://localhost:7868/',\n",
|
||||
" None)"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"io.launch()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6 (tensorflow)",
|
||||
"language": "python",
|
||||
"name": "tensorflow"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
4
gradio/static/js/jquery-3.2.1.slim.min.js
vendored
Normal file
4
gradio/static/js/jquery-3.2.1.slim.min.js
vendored
Normal file
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue
Block a user